Deep Stack Neural Networks Based Learning Model for Fault Detection and Classification in Sensor Data

2021 ◽  
pp. 101-110
Author(s):  
M. Praneesh ◽  
R. Annamalai Saravanan
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Learning Model ◽  
Sensor Data ◽  
Fault Detection And Classification

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Z. N. Sadough Vanini ◽  
N. Meskin ◽  
K. Khorasani
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Data ◽  
Turbine Engines ◽  
Multiple Model ◽  
Jet Engines ◽  
Dynamic Neural Networks ◽  
Autoassociative Neural Networks

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

scholarly journals Fault detection and classification in Industrial IoT in case of missing sensor data

2021 ◽  
Author(s):  
Merim Dzaferagic ◽  
Nicola Marchetti ◽  
Irene Macaluso
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Sensor Data ◽  
Generative Adversarial Networks ◽  
Missing Measurements ◽  
Tennessee Eastman Process ◽  
Fault Detection And Classification ◽  
Industrial Iot ◽  
Critical Components ◽  
The Impact

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

scholarly journals Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Moath Alrifaey ◽  
Wei Hong Lim ◽  
Chun Kit Ang ◽  
Elango Natarajan ◽  
Mahmud Iwan Solihin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Learning Model ◽  
Photovoltaic System ◽  
Fault Detection And Classification ◽  
Deep Learning Model
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